Modern language models (LMs) have been successfully employed in source code
generation and understanding, leading to a significant increase in research focused on …

Abstract Adversarial Machine Learning (AML) discusses the act of attacking and defending
Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML …

Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made
revolutionary progress in recent years, and are widely used in various fields. The high …

Explainable AI encourages machine learning applications in the real world, whereas data-
free model extraction attacks (DFME), in which an adversary steals a trained machine …

The commercial use of machine learning (ML) is spreading; at the same time, ML models
are becoming more complex and more expensive to train, which makes intellectual property …

Recently non-transferable learning (NTL) was proposed to restrict models' generalization
toward the target domain (s) which serves as state-of-the-art solutions for intellectual …

Counterfactual explanations find ways of achieving a favorable model outcome with
minimum input perturbation. However, counterfactual explanations can also be exploited to …

The robustness of recommender systems has become a prominent topic within the research
community. Numerous adversarial attacks have been proposed, but most of them rely on …

Inference is a significant part of ML software infrastructure. Despite the variety of inference
frameworks available, the field as a whole can be considered in its early days. This position …

Numerous works study black-box attacks on image classifiers, where adversaries generate
adversarial examples against unknown target models without having access to their internal …